校正識別模型 的英文怎麼說

中文拼音 [jiàozhēngzhìbiémóxíng]
校正識別模型 英文
correctly identified model
  • 校 : 校名詞1. （學校） school 2. （校官） field officer3. （姓氏） a surname
  • 正 : 正名詞（正月） the first month of the lunar year; the first moon
  • 識 : 識Ⅰ動詞[書面語] （記） remember; commit to memory Ⅱ名詞1. [書面語] （記號） mark; sign 2. （姓氏） a surname
  • 別 : 別動詞[方言] （改變） change （sb. 's opinion）
  • 模 : 模名詞1. （模子） mould; pattern; matrix 2. （姓氏） a surname
  • 校正 : check; correction; adjust; revise; proofread and correct; rectify; calibrate; make true; master c...
  • 識別 : 1 （辯別; 辯認） discriminate; distinguish; discern; tell the difference; spot 2 [計算機] identif...
  • 模型 : 1 （仿製實物） model; pattern 2 （制砂型的工具） mould; pattern3 （模子） model set; mould patter...
  1. Following the research route of mend with study and development with creation, give the definition of risk and the methods of risk identifying, divide the risk attitude into risk loving, risk neutralism and risk avoiding, point out the importance of enhancing the risk consciousness for lightning hazard, and summarize the mechanisms of lightning hazard the theories and methods of risk assessment for lightning hazard. provide a set of risk assessment parameters for lightning hazard, which includes lightning times n, hazard probability p, hazard loss d, hazard risk r and protection efficiency e, and give the definition, decisive factor, value method and value scope of each parameter. establish a risk assessment model for lightning hazard which includes lightning hazard base module, lightning hazard probability module, lightning hazard loss module, lightning hazard accepted risk module, lightning protection cost module, correcting coefficient module, lightning hazard risk module, and lightning protection class and efficiency module

    遵循借鑒改造和發展創新的研究思路，給出了風險的定義和風險識別的方法，將風險態度分為風險喜好型、風險中庸型和風險逃避型，指出了提高雷電災害風險意識的重要性，總結了雷電災害的作用機制和雷電災害風險評估的理論與方法；提供了包括雷擊次數n 、雷災概率p 、雷災損失d 、雷災風險r和雷電防護級別與防護效率e等5類基本參數的雷電災害風險評估參數體系，並給出了各個參數的定義、參數的決定因素和取值方法以及取值范圍；設計了包括雷電災害基礎模塊、雷電災害概率評估模塊、雷電災害損失評估模塊、雷電災害允許風險評估模塊、雷電防護成本評估模塊、校正系數模塊、雷電災害風險評估模塊、雷電防護級別與效率分析模塊等8個模塊的雷電災害風險評估模型，評估模型以iec61662的評估模型為基本參考，以雷災損失d為中心，把雷災風險劃分為經濟雷災風險r _ e和人身雷災風險r _ l ，並對r _ e和r _ l分開單獨處理。
  2. The error accumulation caused by the errors of modal vectors is banished and an emendation to the classical modal model is conducted, due to the extraction of the independent contribution of each mode by means of the orthogonality of the modal filter

    通過對離散模態濾波器的改進，避免了載荷重構階段模態向量誤差引起的載荷識別誤差積累，從而實現了對經典載荷識別模態模型的校正。
  3. Among the arithmetic of air - fuel control, self - learning, self - adapting and self - correction can meet control demand but they also need on - line identifying of object model. they are complicated and need vast operation so they are difficult to realize and need lots of gantry experiments, taking time and hard sledding, which lead to their application success rate under people ' s expectation

    在空燃比控制演算法中，採用自學習、自適應、自校正控制雖然能達到控制要求，但其本質上仍然要求對象模型的在線識別，故演算法復雜，運算量大，難于實現，並需要進行大量的臺架試驗，費時、費力，致使它們的應用成功率遠不如人們期望的那麼高。
  4. Later on, after elaborating the disadvantages of the old methods in detecting and recognizing moving objects, a series of corresponding approaches are proposed, such as grid scan, local tracking bug and dynamic window in object tracing to reduce the huge data needed to be processed, maximum and minimum for selecting a proper segmentation threshold and improved conversion from rgb model to hsv and so on to decrease the influence of inhomogeneous lighting and the color noise, a bilinear interpolation in each quadrant to eliminate the bad effect on the recognition precise because of the distortions of the camera. after that, much emphasis is given on application study in pattern recognition with a feed - forward neural network. both the basic bp algorithm and improved bp algorithm in the study process are described in detail, and the later is used to quicken convergence speed and improve validity of the network

    然後，分析和闡明了傳統的運動目標檢測方法的不足，並在此基礎上結合研究中的實際實驗環境，提出了一系列解決方法，包括針對降低龐大數據量而提出的網格掃描、局部「跟蟲」追蹤和動態窗口掃描等目標檢測方法，針對實驗環境中光照不均和顏色干擾提出基於人機交互的最大最小值閾值選取方法和引入改進的rgb模型到hsv模型的轉換方法，為消除圖像畸變對識別精度的惡劣影響而採用的通過控制點進行雙線性插值進行畸變校正的方法；緊接著，概述了神經網路的發展歷史和幾種常用神經網路模型的特點，重點研究了前饋型神經網路在模式識別中的應用問題，詳細闡述了基本的bp演算法和學習過程中bp演算法的改進，從而使網路收斂速度更快，解決問題更有效，並在此基礎上，設計了一個基於bp神經網路的運動目標識別系統，給出了實驗結果。
分享友人
分享到 Line

分享到臉書